Claude-skill-registry capacity

Analyze service capacity, load patterns, and scaling requirements

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/capacity" ~/.claude/skills/majiayu000-claude-skill-registry-capacity && rm -rf "$T"

manifest: skills/data/capacity/SKILL.md

Capacity Planning Analysis

Current Time: !

date

K8s Context: !

kubectl config current-context

Analyze service capacity, load patterns, and scaling requirements. Generates forecasts and recommendations for resource planning. Documents to Obsidian.

Input

Target: service, cluster, or infrastructure component
Context: current traffic patterns, growth expectations
Optional: specific scaling concerns or upcoming events

Investigation Strategy

Launch parallel investigation tracks:

Track 1: Codebase Exploration (explore agent)

Find autoscaling configuration
Identify resource requests/limits
Locate performance-critical code paths
Map service dependencies

Track 2: Infrastructure Analysis (inferred agent: k8s/gcp-dev/sre)

Review current resource utilization
Analyze scaling policies
Check quota and limits
Assess headroom and buffer

Track 3: External Research (librarian agent)

Find capacity planning best practices
Research scaling patterns for similar workloads
Identify industry benchmarks

Output

Write to Obsidian via

obsidian_append_content

at:

$OBSIDIAN_PATH/Capacity/YYYY-MM-DD-service-analysis.md

Note:
$OBSIDIAN_PATH
must be a vault-relative path (e.g.,
Projects/myapp
), set per-project via direnv. The
obsidian_append_content
tool expects paths relative to the vault root.

Document Structure

Use this template for the Obsidian document:

@~/.config/opencode/templates/capacity-analysis.md

Behavior

Parse target to identify service and infrastructure scope
Infer appropriate infrastructure agent (k8s, gcp-dev, sre)
Launch explore, librarian, and inferred agent in parallel
Analyze current utilization and scaling configuration
Identify load patterns and growth trends
Generate capacity forecast with resource requirements
Identify bottlenecks and risks
Write analysis to Obsidian via
```
obsidian_append_content
```
with auto-generated filename:
```
YYYY-MM-DD-service-analysis.md
```

$ARGUMENTS